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Using Standardized Diagnostic Instruments to Classify Children with Autism in the Study to Explore Early Development

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Abstract

The Study to Explore Early Development (SEED) is a multi-site case–control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) to classify children with ASD, (2) examine psychometric properties of different ASD classification methods, including the SEED method that incorporates rules for resolving ADI-R and ADOS discordance, and (3) determine whether restricted interests and repetitive behaviors were noted for children who had instrument discordance resolved using ADI-R social and communication scores. Results support the utility of SEED criteria when well-defined groups of children are an important clinical or research outcome.

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Acknowledgments

We would like to thank Lisa Croen, Julie Daniels, Ellen Giarelli, Rebecca Landa, Cordelia Robinson, Diana Schendel, Amy Sims, and Patrick Thompson for their contributions to the development of the SEED final classification algorithm and/or comments on previous versions of this paper. We would also like to thank Aimee Alexander, Rebecca Cantrell, and Laura Schieve for their assistance with data cleaning and the SEED principal investigators, co-principal investigators, project coordinators, project staff, and children and families who participated in this research. This publication was supported by six cooperative agreements from the Centers for Disease Control and Prevention: Cooperative Agreement Number U10DD000180, Colorado Department of Public Health; Cooperative Agreement Number U10DD000181, Kaiser Foundation Research Institute (CA); Cooperative Agreement Number U10DD000182, University of Pennsylvania; Cooperative Agreement Number U10DD000183, Johns Hopkins University; Cooperative Agreement Number U10DD000184, University of North Carolina at Chapel Hill; and Cooperative Agreement Number U10DD000498, Michigan State University. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

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Authors and Affiliations

  1. National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 1600 Clifton Road MS E-86, Atlanta, GA, 30333, USA

    Lisa D. Wiggins & Catherine E. Rice

  2. University of Colorado, School of Medicine, Aurora, CO, USA

    Ann Reynolds, Eric J. Moody & Steven A. Rosenberg

  3. Autism Spectrum Disorders Center, San Jose Medical Center, Kaiser Permanente Northern California, San Jose, CA, USA

    Pilar Bernal

  4. Center for Autism Research, Children’s Hospital of Philadelphia, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA

    Lisa Blaskey & Susan E. Levy

  5. Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

    Li-Ching Lee

Authors
  1. Lisa D. Wiggins
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  2. Ann Reynolds
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  3. Catherine E. Rice
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  4. Eric J. Moody
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  5. Pilar Bernal
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  6. Lisa Blaskey
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  7. Steven A. Rosenberg
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  8. Li-Ching Lee
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  9. Susan E. Levy
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Correspondence to Lisa D. Wiggins.

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Wiggins, L.D., Reynolds, A., Rice, C.E. et al. Using Standardized Diagnostic Instruments to Classify Children with Autism in the Study to Explore Early Development. J Autism Dev Disord 45, 1271–1280 (2015). https://doi.org/10.1007/s10803-014-2287-3

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  • DOI: https://doi.org/10.1007/s10803-014-2287-3

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